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Using machine learning to identify the best CRISPR-Cas9 targets for functional gene knockout
EP27256
Using machine learning to identify the best CRISPR-Cas9 targets for functional gene knockout
Submitted on 09 Feb 2018

Jesse Stombaugh, Shawn McClelland, Emily M. Anderson, ┼Żaklina Strezoska, Elena Maksimova, Annaleen Vermeulen, Steve Lenger, Tyler Reed, and Anja van Brabant Smith
Dharmacon part of Horizon Discovery Group
This poster was presented at Keystone 2018
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Poster Abstract
We systematically transfected >1100 synthetic crRNA:tracrRNA targeting components of the proteasome into a reporter cell line in which knockout of proteasome function results in fluorescence of a ubiquitin-EGFP fusion protein that is normally degraded by the proteasome pathway. Using the results from the functional assay, we developed and trained a machine-learning algorithm to score crRNAs based on how likely they were to produce functional knockout of targeted genes (functionality score). To minimize potential off-targets, we developed a rigorous specificity tool that is able to detect and score mismatches as well as gapped alignments that are typically missed using most existing specificity tools (specificity score). We combined this comprehensive specificity check with our functionality algorithm to select and score highly specific and functional crRNAs for any given gene target and also generated a whole-genome arrayed crRNA library for screening applications. Report abuse »
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